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Prediction of Major Regio‐, Site‐, and Diastereoisomers in Diels–Alder Reactions by Using Machine‐Learning: The Importance of Physically Meaningful Descriptors
Author(s) -
Beker Wiktor,
Gajewska Ewa P.,
Badowski Tomasz,
Grzybowski Bartosz A.
Publication year - 2019
Publication title -
angewandte chemie international edition
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.831
H-Index - 550
eISSN - 1521-3773
pISSN - 1433-7851
DOI - 10.1002/anie.201806920
Subject(s) - steric effects , diastereomer , diene , diels–alder reaction , chemistry , core (optical fiber) , quantum chemical , computational chemistry , artificial intelligence , stereochemistry , computer science , organic chemistry , molecule , catalysis , telecommunications , natural rubber
Machine learning can predict the major regio‐, site‐, and diastereoselective outcomes of Diels–Alder reactions better than standard quantum‐mechanical methods and with accuracies exceeding 90 % provided that i) the diene/dienophile substrates are represented by “physical‐organic” descriptors reflecting the electronic and steric characteristics of their substituents and ii) the positions of such substituents relative to the reaction core are encoded (“vectorized”) in an informative way.